Pedagogical learnings for management education : developing creativity and innovation

This paper argues that management education needs to consider a trend in learning design which advances creative learning through an alliance with art-based pedagogical processes. A shift is required from skills training to facilitating transformational learning through experiences that expand human potential, facilitated by artistic processes. This creative learning focus stems from a qualitative and quantitative analysis of an arts-based intervention for management development, called Management Jazz, conducted over three years at a large Australian University. The paper reviews some of the salient literature in the field, including an ‘Artful Learning Wave Trajectory’ Model. The Model considers four stages of the learning process: capacity, artful event, increased capability, and application/action to produce product. Methodology for the field-based research analysis of the intervention outcomes is presented. Three illustrative examples of arts-based learning are provided from the Management Jazz program. Finally, research findings indicate that artful learning opportunities enhance capacity for awareness of creativity in one’s self and in others, leading, through a transformative process, to enhanced leaders and managers. The authors conclude that arts-based management education can enhance creative capacity and develop managers and leaders for the 21st century business environment.

Implicit discount rates and fisheries management : is there a relationship?

Fishers are faced with multiple risks, including unpredictability of future catch rates, prices and costs. While the latter are largely beyond the control of fisheries managers, effective fisheries management should reduce uncertainty about future catches. Different management instruments are likely to have different impacts on the risk perception of fishers, and this should manifest itself in their implicit discount rate. Assuming licence and quota values represent the net present value of the flow of expected future profits, then a proxy for the implicit discount rate of vessels in a fishery can be derived by the ratio of the average level of profits to the average licence/quota value. From this, an indication of the risk perception can be derived, assuming higher discount rates reflect higher levels of systematic risk. In this paper, we apply the capital asset pricing model (CAPM) to determine the risk premium implicit in the discount rates for a range of Australian fisheries, and compare this with the set of management instruments in place. We test the assumption that rights based management instruments lower perceptions of risk in fisheries. We find little evidence to support this assumption. although the analysis was based on only limited data.

Introduction and Epilogue to Special Issue: Re-conceiving the artful in management development and education

The editorial focus of this issue is on artful, aesthetic and artistic endeavours in management. Being artful is not about arts-based quick fixes. In the context of this Special Issue, to be artful is to transform self through profound learning experiences that expand human consciousness, often facilitated by artistic processes. In management education and development this suggests a shift from instrumental management towards a paradigm of artful creation. Why the arts and artfulness? And why now? In what ways can the arts inform, inspire and leverage management development and education?

A Two-stage information filtering based on rough decision rule and pattern mining

Information Overload and Mismatch are two fundamental problems affecting the effectiveness of information filtering systems. Even though both term-based and patternbased approaches have been proposed to address the problems of overload and mismatch, neither of these approaches alone can provide a satisfactory solution to address these problems. This paper presents a novel two-stage information filtering model which combines the merits of term-based and pattern-based approaches to effectively filter sheer volume of information. In particular, the first filtering stage is supported by a novel rough analysis model which efficiently removes a large number of irrelevant documents, thereby addressing the overload problem. The second filtering stage is empowered by a semantically rich pattern taxonomy mining model which effectively fetches incoming documents according to the specific information needs of a user, thereby addressing the mismatch problem. The experimental results based on the RCV1 corpus show that the proposed twostage filtering model significantly outperforms the both termbased and pattern-based information filtering models.

The ecology of Bactrocera tryoni (Froggatt) (Diptera: Tephritdae) : what do we know to assist pest management?

The distribution, systematics and ecology of Bactrocera tryoni, the Queensland fruit fly are reviewed. Bactrocera tryoni is a member of the B. tryoni complex of species, which currently includes four named species, viz. B. tryoni s.s., B. neohumeralis, B. melas and B. aquilonis. The species status of B. melas and B. aquilonis are unclear (they may be junior synonyms of B. tryoni) and their validity, or otherwise, needs to be confirmed as a matter of urgency. While Queensland fruit fly is regarded as a tropical species, it cannot be assumed that its distribution will spread further south under climate change scenarios. Increasing aridity and hot dry summers, as well as more complex, indirect interactions resulting from elevated CO2, make predicting the future distribution and abundance of B. tryoni difficult. The ecology of B. tryoni is reviewed with respect to current control approaches (with the exception of Sterile Insect Technique which is covered in a companion paper). We conclude that there are major gaps in the knowledge required to implement most non-insecticide based management approaches. Priority areas for future research include host plant interactions, protein and cue-lure foraging and use, spatial dynamics, development of new monitoring tools, investigating the use of natural enemies and better integration of fruit flies into general horticultural IPM systems.

QoS-aware web service composition using genetic algorithms

Web service technology is increasingly being used to build various e-Applications, in domains such as e-Business and e-Science. Characteristic benefits of web service technology are its inter-operability, decoupling and just-in-time integration. Using web service technology, an e-Application can be implemented by web service composition — by composing existing individual web services in accordance with the business process of the application. This means the application is provided to customers in the form of a value-added composite web service. An important and challenging issue of web service composition, is how to meet Quality-of-Service (QoS) requirements. This includes customer focused elements such as response time, price, throughput and reliability as well as how to best provide QoS results for the composites. This in turn best fulfils customers’ expectations and achieves their satisfaction. Fulfilling these QoS requirements or addressing the QoS-aware web service composition problem is the focus of this project. From a computational point of view, QoS-aware web service composition can be transformed into diverse optimisation problems. These problems are characterised as complex, large-scale, highly constrained and multi-objective problems. We therefore use genetic algorithms (GAs) to address QoS-based service composition problems. More precisely, this study addresses three important subproblems of QoS-aware web service composition; QoS-based web service selection for a composite web service accommodating constraints on inter-service dependence and conflict, QoS-based resource allocation and scheduling for multiple composite services on hybrid clouds, and performance-driven composite service partitioning for decentralised execution. Based on operations research theory, we model the three problems as a constrained optimisation problem, a resource allocation and scheduling problem, and a graph partitioning problem, respectively. Then, we present novel GAs to address these problems. We also conduct experiments to evaluate the performance of the new GAs. Finally, verification experiments are performed to show the correctness of the GAs. The major outcomes from the first problem are three novel GAs: a penaltybased GA, a min-conflict hill-climbing repairing GA, and a hybrid GA. These GAs adopt different constraint handling strategies to handle constraints on interservice dependence and conflict. This is an important factor that has been largely ignored by existing algorithms that might lead to the generation of infeasible composite services. Experimental results demonstrate the effectiveness of our GAs for handling the QoS-based web service selection problem with constraints on inter-service dependence and conflict, as well as their better scalability than the existing integer programming-based method for large scale web service selection problems. The major outcomes from the second problem has resulted in two GAs; a random-key GA and a cooperative coevolutionary GA (CCGA). Experiments demonstrate the good scalability of the two algorithms. In particular, the CCGA scales well as the number of composite services involved in a problem increases, while no other algorithms demonstrate this ability. The findings from the third problem result in a novel GA for composite service partitioning for decentralised execution. Compared with existing heuristic algorithms, the new GA is more suitable for a large-scale composite web service program partitioning problems. In addition, the GA outperforms existing heuristic algorithms, generating a better deployment topology for a composite web service for decentralised execution. These effective and scalable GAs can be integrated into QoS-based management tools to facilitate the delivery of feasible, reliable and high quality composite web services.

Risk-informed thinking in airports: the application of business continuity management in airport infrastructure

Airports worldwide represent key forms of critical infrastructure in addition to serving as nodes in the international aviation network. While the continued operation of airports is critical to the functioning of reliable air passenger and freight transportation, these infrastructure systems face a number of sources of disturbance that threaten their operational viability. Recent examples of high magnitude events include the eruption of Iceland’s Eyjafjallajokull volcano eruption (Folattau and Schofield 2010), the failure of multiple systems at the opening of Heathrow’s Terminal 5 (Brady and Davies 2010) and the Glasgow airport 2007 terrorist attack (Crichton 2008). While these newsworthy events do occur, a multitude of lower-level more common disturbances also have the potential to cause significant discontinuity to airport operations. Regional airports face a unique set of challenges, particularly in a nation like Australia where they serve to link otherwise remote and isolated communities to metropolitan hubs (Wheeler 2005), often without the resources and political attention received by larger capital city airports. This paper discusses conceptual relationships between Business Continuity Management (BCM) and High Reliability Theory, and proposes BCM as an appropriate risk-based management process to ensure continued airport operation in the face of uncertainty. In addition, it argues that that correctly implemented BCM can lead to highly reliable organisations. This is framed within the broader context of critical infrastructures and the need for adequate crisis management approaches suited to their unique requirements (Boin and McConnell 2007).

Challenging the female underperformance hypothesis

In their recent review of prior studies examining firm performance, Klapper and Parker (2010, p.7) conclude that “women entrepreneurs tend to underperform relative to their male counterparts.” However, Robb and Watson (2011) argue that much of this prior research is based on inappropriate performance measures and/or does not adequately control (due to data limitations) for important demographic differences. Given the conflicting findings reported in the literature, the aim of this study is to replicate the study by Robb and Watson (2011) to see if their findings can be generalized to another geographical location. Our results, based on an analysis of 209 female-owned and 263 male-owned young Australian firms, confirm those of Robb and Watson (2011). We believe that this outcome should help dispel the female underperformance myth; which if left unchallenged could result in inappropriate policy decisions and, more importantly, could discourage women from establishing new ventures.

The effect of exercise training on sleep quality in heart failure

Background: Heart failure is a serious condition estimated to affect 1.5-2.0% of the Australian population with a point prevalence of approximately 1% in people aged 50-59 years, 10% in people aged 65 years or more and over 50% in people aged 85 years or over (National Heart Foundation of Australian and the Cardiac Society of Australia and New Zealand, 2006). Sleep disturbances are a common complaint of persons with heart failure. Disturbances of sleep can worsen heart failure symptoms, impair independence, reduce quality of life and lead to increased health care utilisation in patients with heart failure. Previous studies have identified exercise as a possible treatment for poor sleep in patients without cardiac disease however there is limited evidence of the effect of this form of treatment in heart failure. Aim: The primary objective of this study was to examine the effect of a supervised, hospital-based exercise training programme on subjective sleep quality in heart failure patients. Secondary objectives were to examine the association between changes in sleep quality and changes in depression, exercise performance and body mass index. Methods: The sample for the study was recruited from metropolitan and regional heart failure services across Brisbane, Queensland. Patients with a recent heart failure related hospital admission who met study inclusion criteria were recruited. Participants were screened by specialist heart failure exercise staff at each site to ensure exercise safety prior to study enrolment. Demographic data, medical history, medications, Pittsburgh Sleep Quality Index score, Geriatric Depression Score, exercise performance (six minute walk test), weight and height were collected at Baseline. Pittsburgh Sleep Quality Index score, Geriatric Depression Score, exercise performance and weight were repeated at 3 months. One hundred and six patients admitted to hospital with heart failure were randomly allocated to a 3-month disease-based management programme of education and self-management support including standard exercise advice (Control) or to the same disease management programme as the Control group with the addition of a tailored physical activity program (Intervention). The intervention consisted of 1 hour of aerobic and resistance exercise twice a week. Programs were designed and supervised by an exercise specialist. The main outcome measure was achievement of a clinically significant change (.3 points) in global Pittsburgh Sleep Quality score. Results: Intervention group participants reported significantly greater clinical improvement in global sleep quality than Control (p=0.016). These patients also exhibited significant improvements in component sleep disturbance (p=0.004), component sleep quality (p=0.015) and global sleep quality (p=0.032) after 3 months of supervised exercise intervention. Improvements in sleep quality correlated with improvements in depression (p<0.001) and six minute walk distance (p=0.04). When study results were examined categorically, with subjects classified as either "poor" or "good" sleepers, subjects in the Control group were significantly more likely to report "poor" sleep at 3 months (p=0.039) while Intervention participants were likely to report "good" sleep at this time (p=0.08). Conclusion: Three months of supervised, hospital based, aerobic and resistance exercise training improved subjective sleep quality in patients with heart failure. This is the first randomised controlled trial to examine the role of aerobic and resistance exercise training in the improvement of sleep quality for patients with this disease. While this study establishes exercise as a therapy for poor sleep quality, further research is needed to investigate the effect of exercise training on objective parameters of sleep in this population.

Agent-based model of passenger flows in airport terminals

Passenger flow studies in airport terminals have shown consistent statistical relationships between airport spatial layout and pedestrian movement, facilitating prediction of movement from terminal designs. However, these studies are done at an aggregate level and do not incorporate how individual passengers make decisions at a microscopic level. Therefore, they do not explain the formation of complex movement flows. In addition, existing models mostly focus on standard airport processing procedures such as immigration and security, but seldom consider discretionary activities of passengers, and thus are not able to truly describe the full range of passenger flows within airport terminals. As the route-choice decision-making of passengers involves many uncertain factors within the airport terminals, the mechanisms to fulfill the capacity of managing the route-choice have proven difficult to acquire and quantify. Could the study of cognitive factors of passengers (i.e. human mental preferences of deciding which on-airport facility to use) be useful to tackle these issues? Assuming the movement in virtual simulated environments can be analogous to movement in real environments, passenger behaviour dynamics can be similar to those generated in virtual experiments. Three levels of dynamics have been devised for motion control: the localised field, tactical level, and strategic level. A localised field refers to basic motion capabilities, such as walking speed, direction and avoidance of obstacles. The other two fields represent cognitive route-choice decision-making. This research views passenger flow problems via a "bottom-up approach", regarding individual passengers as independent intelligent agents who can behave autonomously and are able to interact with others and the ambient environment. In this regard, passenger flow formation becomes an emergent phenomenon of large numbers of passengers interacting with others. In the thesis, first, the passenger flow in airport terminals was investigated. Discretionary activities of passengers were integrated with standard processing procedures in the research. The localised field for passenger motion dynamics was constructed by a devised force-based model. Next, advanced traits of passengers (such as their desire to shop, their comfort with technology and their willingness to ask for assistance) were formulated to facilitate tactical route-choice decision-making. The traits consist of quantified measures of mental preferences of passengers when they travel through airport terminals. Each category of the traits indicates a decision which passengers may take. They were inferred through a Bayesian network model by analysing the probabilities based on currently available data. Route-choice decision-making was finalised by calculating corresponding utility results based on those probabilities observed. Three sorts of simulation outcomes were generated: namely, queuing length before checkpoints, average dwell time of passengers at service facilities, and instantaneous space utilisation. Queuing length reflects the number of passengers who are in a queue. Long queues no doubt cause significant delay in processing procedures. The dwell time of each passenger agent at the service facilities were recorded. The overall dwell time of passenger agents at typical facility areas were analysed so as to demonstrate portions of utilisation in the temporal aspect. For the spatial aspect, the number of passenger agents who were dwelling within specific terminal areas can be used to estimate service rates. All outcomes demonstrated specific results by typical simulated passenger flows. They directly reflect terminal capacity. The simulation results strongly suggest that integrating discretionary activities of passengers makes the passenger flows more intuitive, observing probabilities of mental preferences by inferring advanced traits make up an approach capable of carrying out tactical route-choice decision-making. On the whole, the research studied passenger flows in airport terminals by an agent-based model, which investigated individual characteristics of passengers and their impact on psychological route-choice decisions of passengers. Finally, intuitive passenger flows in airport terminals were able to be realised in simulation.

Product feature taxonomy learning based on user reviews

In recent years, the Web 2.0 has provided considerable facilities for people to create, share and exchange information and ideas. Upon this, the user generated content, such as reviews, has exploded. Such data provide a rich source to exploit in order to identify the information associated with specific reviewed items. Opinion mining has been widely used to identify the significant features of items (e.g., cameras) based upon user reviews. Feature extraction is the most critical step to identify useful information from texts. Most existing approaches only find individual features about a product without revealing the structural relationships between the features which usually exist. In this paper, we propose an approach to extract features and feature relationships, represented as a tree structure called feature taxonomy, based on frequent patterns and associations between patterns derived from user reviews. The generated feature taxonomy profiles the product at multiple levels and provides more detailed information about the product. Our experiment results based on some popularly used review datasets show that our proposed approach is able to capture the product features and relations effectively.

CoRE : a context-aware relation extraction method for relation completion

We identify relation completion (RC) as one recurring problem that is central to the success of novel big data applications such as Entity Reconstruction and Data Enrichment. Given a semantic relation, RC attempts at linking entity pairs between two entity lists under the relation. To accomplish the RC goals, we propose to formulate search queries for each query entity α based on some auxiliary information, so that to detect its target entity β from the set of retrieved documents. For instance, a pattern-based method (PaRE) uses extracted patterns as the auxiliary information in formulating search queries. However, high-quality patterns may decrease the probability of finding suitable target entities. As an alternative, we propose CoRE method that uses context terms learned surrounding the expression of a relation as the auxiliary information in formulating queries. The experimental results based on several real-world web data collections demonstrate that CoRE reaches a much higher accuracy than PaRE for the purpose of RC.

The impact of incidents on macroscopic fundamental diagrams

The macroscopic fundamental diagram (MFD) traffic modelling method has been proved for large urban roads and freeway networks, but hysteresis and scatter have been found in both such networks. This paper investigates how incident variables affect the shape and scatter of the MFD using both simulated data and real data collected from the M3 Pacific motorway in Brisbane, Australia. Three key components of incidents are investigated based on the simulated data (i.e. incident location, incident duration and traffic demand). The results based on simulated data indicate that the diagram shape is a property not only of the network itself but also of the incident variables. Diagrams for three types of real incidents (crash, hazard and vehicle breakdown) are explored separately. The results based on the empirical data are consistent with the simulated results. The hysteresis phenomenon occurs both upstream and downstream of the incident location, but for opposite hysteresis loops. The gradient of the upstream diagram is greater than that downstream on the incident site, when traffic demand is for an off-peak period.

Some comparison on whole-proteome phylogeny of large dsDNA viruses based on dynamical language approach and feature frequency profiles method

There has been a growing interest in alignment-free methods for phylogenetic analysis using complete genome data. Among them, CVTree method, feature frequency profiles method and dynamical language approach were used to investigate the whole-proteome phylogeny of large dsDNA viruses. Using the data set of large dsDNA viruses from Gao and Qi (BMC Evol. Biol. 2007), the phylogenetic results based on the CVTree method and the dynamical language approach were compared in Yu et al. (BMC Evol. Biol. 2010). In this paper, we first apply dynamical language approach to the data set of large dsDNA viruses from Wu et al. (Proc. Natl. Acad. Sci. USA 2009) and compare our phylogenetic results with those based on the feature frequency profiles method. Then we construct the whole-proteome phylogeny of the larger dataset combining the above two data sets. According to the report of The International Committee on the Taxonomy of Viruses (ICTV), the trees from our analyses are in good agreement to the latest classification of large dsDNA viruses.

A recommendation system for predicting risks across multiple business process instances

This paper proposes a recommendation system that supports process participants in taking risk-informed decisions, with the goal of reducing risks that may arise during process execution. Risk reduction involves decreasing the likelihood and severity of a process fault from occurring. Given a business process exposed to risks, e.g. a financial process exposed to a risk of reputation loss, we enact this process and whenever a process participant needs to provide input to the process, e.g. by selecting the next task to execute or by filling out a form, we suggest to the participant the action to perform which minimizes the predicted process risk. Risks are predicted by traversing decision trees generated from the logs of past process executions, which consider process data, involved resources, task durations and other information elements like task frequencies. When applied in the context of multiple process instances running concurrently, a second technique is employed that uses integer linear programming to compute the optimal assignment of resources to tasks to be performed, in order to deal with the interplay between risks relative to different instances. The recommendation system has been implemented as a set of components on top of the YAWL BPM system and its effectiveness has been evaluated using a real-life scenario, in collaboration with risk analysts of a large insurance company. The results, based on a simulation of the real-life scenario and its comparison with the event data provided by the company, show that the process instances executed concurrently complete with significantly fewer faults and with lower fault severities, when the recommendations provided by our recommendation system are taken into account.